Title

Compression of medical images using Local Neighbor Difference

Date of Award

2017

Degree Name

M.S. in Electrical Engineering

Department

Department of Electrical and Computer Engineering

Advisor/Chair

Advisor: Eric John Balster

Abstract

Medical images are an essential part to any health professional's career when helping patients and diagnosing health concerns. Due to the need for large storage capacity and fast transferring speed, research in image compression has grown. Image compression uses the property of redundant information in the image to reduce the amount of data in the image to solve both problems of storage and transmission. For medical images, lossless compression algorithms are of interest to make sure that the reconstructed image provides the same details as the original image. This thesis presents a proposed algorithm called the Local Neighbor Difference (LND) which is a preprocessing technique to allow the redundancy in the medical image to be reduced before being sent into a commercial-off-the-shelf compressor (COTS), XZ. LND, when used in conjunction with XZ losslessly, compresses images, on average, by 6% more than XZ alone. The LND process, along with some future work, is proposed in this paper and results in a viable option for a pre-process to a compressor.

Keywords

Image compression Data processing, Computer algorithms, Imaging systems Image quality, Electrical Engineering, medical images, lossless, image compression, XZ

Rights Statement

Copyright 2017, author

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